biostudies-literatureSpeyer LGCanton of Zurich’s Department of Education, the Swiss Federal Commission on MigrationSwiss National Science FoundationJacobs FoundationProductive Youth Development, the Swiss Federal Office of Public Health328-344https://www.ebi.ac.uk/biostudies/studies/S-EPMC11996063biostudies-literatureUnknown60(2)Random-intercept cross-lagged panel models (RI-CLPMs) are increasingly used to investigate research questions focusing on how one variable at one time point affects another variable at the subsequent time point. Due to the implied temporal sequence of events in such research designs, interpretations of RI-CLPMs primarily focus on longitudinal cross-lagged paths while disregarding concurrent associations and modeling these only as residual covariances. However, this may cause biased cross-lagged effects. This may be especially so when data collected at the same time point refers to different reference timeframes, creating a temporal sequence of events for constructs measured concurrently. To examine this issue, we conducted a series of empirical analyses in which the impact of modeling or nMultivariate behavioral researchOn the Importance of Considering Concurrent Effects in Random-Intercept Cross-Lagged Panel Modelling: Example Analysis of Bullying and Internalising Problems.PMC11996063E-05--129,09, 10FI14_91, 8.24,79,-40-03-901 (IMES-888,5,14_2.13_5279, 10FI14_65Yang YEisner MZhu XRibeaud DSpeyer LGfalseOn the Importance of Considering Concurrent Effects in Random-Intercept Cross-Lagged Panel Modelling: Example Analysis of Bullying and Internalising Problems.Random-intercept cross-lagged panel models (RI-CLPMs) are increasingly used to investigate research questions focusing on how one variable at one time point affects another variable at the subsequent time point. Due to the implied temporal sequence of events in such research designs, interpretations of RI-CLPMs primarily focus on longitudinal cross-lagged paths while disregarding concurrent associations and modeling these only as residual covariances. However, this may cause biased cross-lagged effects. This may be especially so when data collected at the same time point refers to different reference timeframes, creating a temporal sequence of events for constructs measured concurrently. To examine this issue, we conducted a series of empirical analyses in which the impact of modeling or n2025-01-01T00:00:00Z2025 Mar-Apr2025-06-26T03:06:24.088Z2025-06-26T03:06:24.088ZS-EPMC119960633958879410.1080/00273171.2024.2428222